An explosion of computing devices, stationary and portable, is currently underway. These computing devices include, but are not limited to, smartphones, laptops, tablets, phablets, etc. Demand for these devices is made more acute by social media's rapid evolution, which encourages a need to communicate and be in contact with others. This creates continual competition among various manufacturers to generate better and faster computing devices.
However, an issue associated with this development that becomes more acute in dense urban locations is the poor cell coverage for small geographical pockets that are either in the shadow of a large building, or inside a large building, effects known in the field as “street canyon” and/or “building shadowing.” To address the poor or lack of coverage in these pockets, the modern mobile telecommunication networks have started to deploy small cells, in addition to the traditional macro cells.
Such a heterogeneous telecommunication network 100 is schematically illustrated in FIG. 1, in which a macro cell 102 (only one macro cell is illustrated for simplicity) is located on a building 104 in a densely built up area 106. This area includes streets 108 on which high buildings 110 are located. Due to these high-rise buildings, there might be areas 120 that have poor or no signal from macro cell 102. Deploying another macro cell for these pockets of no or poor cell coverage is not efficient, both from a financial and a frequency bandwidth point of view. Thus, there is a trend to deploy a small cell 122 in or close to the area 120 having poor cell coverage.
Regarding the macro and small cells, a small cell may operate in the 10 m to 2 km range while a macro cell may operate in the tens of kilometers. However, note that there is no one agreed upon definition in the industry for the small and macro cell ranges and the numbers provided herein are for exemplary purposes.
However, by deploying many small cells to improve coverage, it leads to higher inter-cell interference levels as well as increased complexity in interference management features to achieve the best performance.
One way to implement a heterogeneous network is to use a base station architecture based on remote radio equipment and radio equipment controllers. For example, as illustrated in FIG. 2, macro cell 102 includes a base station 202 that has the remote radio equipment 204 including radio frequency generation unit 206 and, possibly, antenna elements 208 located in one place while the radio equipment controller 210, which typically includes baseband signal processing units, may be located in another place. The remote radio equipment 204 and radio equipment controller 210 are typically interconnected by fiber 212 carrying user plane information of in-phase and quadrature modulation data (digital baseband signals). Because of the fiber, the two locations can be far away from each other.
By using this approach, a centralized radio access network (C-RAN) 220 with common baseband units 210 for multiple base stations 202′ to 202″ can be used for a large number of macro and small cells covering a larger geographical area. With the C-RAN 220, inter-cell interference can be efficiently managed through interference mitigation features, such as Coordinated Multi-Point (CoMP) for reception and transmission. Note that the main idea behind CoMP is as follows: when user equipment (UE) is in the cell-edge region, it may be able to receive signals from multiple cell sites and the UE's transmission may be received at multiple cell sites regardless of the system load. Thus, if the signals transmitted from the multiple cell sites are coordinated, the downlink performance can be increased significantly. Further algorithms may be used for interference management when the macro and small cells use the same baseband units.
Other interference coordination techniques may be used when macro and small cells use different baseband units (i.e., radio equipment controllers). In this case, there need to be a short interconnecting delay and high transport capacity on the X2 interface (e.g., the fiber 212) or other similar interface between the baseband units.
However, a common problem with applying the existing interference coordination techniques to the various implementations of heterogeneous networks is the following. The existing interference techniques manage the multiple cells (macro and small) by using coordination, i.e., identification of the cells that are responsible for the highest amount of interference. In other words, for a given cell in the telecommunication network, the existing interference techniques need to determine and group those neighbor cells that generate the largest amount of interference. Thus, these techniques require (i) that the macro and small cells share the same radio equipment controller or (ii) there is a fast interconnect between radio equipment controllers when separate baseband units are deployed.
However, there is a practical limit on the number of remote radio equipment that can be connected to the same radio equipment controller or a group of radio equipment controllers. Also, the delay between coordinated cells needs to be below a limit or otherwise the coordination cannot be efficient. For example, a signal copy arriving outside a cyclic prefix in Long Term Evolution (LTE) is treated as interference by the receiver. There can also be a limit regarding the number of connections between the radio equipment controller and the multiple cells due to hardware and/or software limitations (processing power, memory, interconnection interface speed etc.).
A problem is thus to determine what remote radio equipment 204 (used for macro and small cell areas) that shares the same baseband unit 210 should belong to a same cell coordination group to benefit from CoMP and other interference management techniques. If the heterogeneous network has remote radio equipment 204 that do not share the baseband unit 210, then, the problem is what baseband units should belong to the same cell coordination group to benefit the interference management.
As now discussed, the existing methods for generating the cell coordination group have their own limitations. Such a method needs to identify the cells that generate a lot of interference to maximize the efficiency of the interference management. This identification is difficult in city environments due to e.g., street canyon and/or building shadowing effects as discussed with regard to FIG. 1. The geographically closest cell may not be the most interfering cell. To illustrate this concept, FIG. 3 shows a heterogeneous network 300 having macro cells 302 and small cells 304. Small cells 304 are connected by a line to the macro cell that generates the most downlink interference. In many cases, this is not the closest (in term of geographical distance) macro cell. Solid lines 310 are used when the most interfering macro cell also is the closest one and dash lines 312 otherwise. Dash lines 312 are dominating the picture, meaning that the majority of the small cells does not receive the strongest interference from the geographically closest macro cell.
Thus, choosing the closest cells for coordinated interference management will not be a good solution in many cases. Hence, there is a need to develop another method and mechanism for more accurately generating a cell coordination group for interference management activities.